Abstract

Implicit solvent methods for classical molecular modeling are frequently used to provide fast, physics-based hydration free energies of macromolecules. Less commonly considered is the transferability of these methods to other solvents. The Statistical Assessment of Modeling of Proteins and Ligands 5 (SAMPL5) distribution coefficient dataset and the accompanying explicit solvent partition coefficient reference calculations provide a direct test of solvent model transferability. Here we use the 3D reference interaction site model (3D-RISM) statistical-mechanical solvation theory, with a well tested water model and a new united atom cyclohexane model, to calculate partition coefficients for the SAMPL5 dataset. The cyclohexane model performed well in training and testing (\(R=0.98\) for amino acid neutral side chain analogues) but only if a parameterized solvation free energy correction was used. In contrast, the same protocol, using single solute conformations, performed poorly on the SAMPL5 dataset, obtaining \(R=0.73\) compared to the reference partition coefficients, likely due to the much larger solute sizes. Including solute conformational sampling through molecular dynamics coupled with 3D-RISM (MD/3D-RISM) improved agreement with the reference calculation to \(R=0.93\). Since our initial calculations only considered partition coefficients and not distribution coefficients, solute sampling provided little benefit comparing against experiment, where ionized and tautomer states are more important. Applying a simple \(\hbox {p}K_{\text {a}}\) correction improved agreement with experiment from \(R=0.54\) to \(R=0.66\), despite a small number of outliers. Better agreement is possible by accounting for tautomers and improving the ionization correction.

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Acknowledgments

The authors would like to thank Caitlin C. Bannan and David L. Mobley for access to the results from their explicit solvent reference calculations. T.L. would like to additionally thank David Mobley and Stefan Kast for useful discussions about calculating solvation free energies from time series. This work was partially supported by the California State University Program for Education and Research in Biotechnology (CSUPERB; T.L., G.C.L., K.P.J.) and by the Alberta Prion Research Institute and the National Research Council of Canada (N.B., A.K.).